Sleep Apnea Event Prediction Using Convolutional Neural Networks and Markov Chains

Obstructive sleep apnea is a breathing disorder affecting 2-4% of the adult population. It is characterized by periods of reduced breathing (hypopnea) or no breathing (apnea). Several machine learning algorithms have been proposed to automatically classify sleep apnea events, but little work has been done on predicting such events in advance, which is important for the treatment of sleep apnea, and especially for the development of auto-adjusting airway pressure devices to maintain continuous airflow during sleep. In this paper, we propose three methods for predicting sleep apnea events, based on convolution neural networks and Markov chains. Specifically, we use data from respiratory signals (nasal flow, abdominal and thoracic) to predict apnea and hypopnea events in a 30-second period using the prior 60 seconds' data. We evaluate the performance of the proposed methods for automatically learning the required features and predicting the sleep apnea events on a large dataset containing 48,000 examples from 1,507 subjects. The results show the effectiveness of the proposed convolutional neural network method, which achieved accuracy of 80.78% and F1 score of 80.63%. We also analyse the Markov chain rules and provide an overview of the transitions between apnea and normal events.